code: def _unique(
      x, out_idx=np.int32, name=None
  ):  # pylint: disable=unused-argument
      """Numpy implementation of `tf.unique`."""
      x = np.array(x)
      if len(x.shape) != 1:
          raise errors.InvalidArgumentError(
              "unique expects a 1D vector."
          )
      y, idx = np.unique(
          x,
          return_index=True,
          return_inverse=False,
          return_counts=False,
          axis=None,
      )
      idx = idx.astype(utils.numpy_dtype(out_idx))
      return _UniqueOutput(y=y, idx=idx)
  
file_path: probability/tensorflow_probability/python/internal/backend/numpy/misc.py
imports: import collections
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import _utils
  from tensorflow_probability.python.internal.backend.numpy import errors
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import floor
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import truediv
  from tensorflow_probability.python.internal.backend.numpy.ops import _convert_to_tensor
  from tensorflow_probability.python.internal.backend.numpy.ops import clip_by_value
  from tensorflow_probability.python.internal.backend.numpy.ops import is_tensor
label_desc: Use image node_id 9 for calling a global function with example usage: _unique(x, out_idx, name) and returns: _UniqueOutput